Conventional color controls architectures and algorithms do not offer pixel level processing with the advantages of synchronization amongst workers operating on pieces of an image and automated techniques for scalable parallelization. Image processing, especially color controls, on pixel ensembles of the order of 108-109 per image are highly data-intensive and computation-intensive requiring scalable parallelization techniques. Some algorithms are not designed to operate in parallel, especially offering advantages like synchronization. The type of parallelization required to address these problems is called connected parallel (involving inter-processor communication) in addition to data parallel stages. Automatically dealing with connected parallel computation is generally hard in parallelization and have to be specifically addressed for the data sizes we consider in color control. Approximation techniques such as interpolation are used in color controls, some of them to deal with the large image sizes. The use of such techniques may result in artifacts and adversely affect in image quality. However, it is desirable to avoid interpolation by processing each pixel individually. Due to large image sizes, failure in a certain stage of processing is not uncommon. A number of such problems at the stage level is because of the asynchronous nature of the processing and could benefit from a small number of retries.
What is needed in this art are increasingly sophisticated systems and methods which facilitates parallel processing of intra-image data in a distributed computing environment.